Implications of study - Feedback by the soil microbial community on the distribution of two wee

The results have important implications for investigation and management of agricultural weeds. In this study, I found that the spatial variation of weed feedback is correlated with the source microbial community, the presence and abundance of key microbial taxa, and the differences between bacteria and fungi. Future weed studies might consider the following suggestions.

The local microbial community is an important agent of plant feedback. My results indicate that initial microbial communities significantly influence the microbial taxa that could feed back to alter the growth of weeds. This influence might be even stronger than that of plants (Table 6.3 and 6.4). Thus, the success of weed growth depends on which

microbial communities are available and what interactions these microbes will form with plants. Establishing weed-microbe interactions based on the original soil communities before plant is introduced could help provide an explanation of why one weed can grow better in one area than in another (Andonian et al., 2011). It is also highly recommended to have a specific weed management technique applied to each weed growing area.

There are different mechanisms to generate feedback by key microbial species. For example, the emergence of weeds could be a result of direct effects of weed beneficial microbial taxa or could be caused by indirect effects. The indirect effects are derived from the fact that some weeds have a higher tolerance to soil pathogens than competing plant species, so the weed gains positive feedback. In the areas where feedback is caused by direct effects, identifying the beneficial microbial strains of the weed may be helpful to explain weed performance. In contrast, in sites where indirect effects are dominant, management strategies are complicated. Increasing crop pathogen resistance and removing pathogens might need to be considered simultaneously.

Both bacterial and fungal communities are powerful agents correlated with feedback.

Current research has mostly focused on fungal effects on modifying weed populations in agro-ecosystems (Jordan & Huerd, 2008; Jordan et al., 2000; Vatovec et al., 2005); while the bacteria-plant relationships are generally overlooked (Chee-Sanford et al., 2006;

Kremer & Kennedy, 1996). However, this study showed that there are significant correlations between bacteria and feedback. Therefore, more studies are needed to understand the critical roles of key bacteria taxa.

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Figures and Tables

Figures

Figure 6.1. Example boxplot of the feedback score of positive feedback plant. Plant-Soil Feedback score = plant biomass in home soil (plants that experienced same plant in soil training) – mean plant biomass in away soil (plants that experienced different plant in soil training)

Figure 6.2. Workflow of plant-soil feedback experiment. One pot represents 10 replicates in the experiment. This experiment was conducted in the greenhouses of each state based on the same experimental protocols.

10 weeks

Figure 6.3. NMDS ordination plot of all (A) bacterial and (B) fungal ARISA profiles collected from six states (including replicate experimental runs). Each point represents a microbial community.

NMDS axis-1

NMDS axis-2

IL-1 IL-2 KS-1

KS-2

OR SD

MI-1 MT

MI-2

NMDS axis-1

NMDS axis-2

IL-1 IL-2 KS-1 KS-2 OR

SD MI-1

MT MI-2

A. Bacteria

B. Fungi

Component cutoff loading

A B C D E F

0.2:0.2:0.2 0.175:0.175:0.175 0.15:0.15:0.15 Q5%: 0.2:0.2 Q5%:0.15:0.15 Q5%: Q5%: Q5%

Bacteria # 7 17 25 26 33 55

Fung i# 17 22 31 30 38 46

Figure 6.4. Prediction of linear discriminant analysis and general linear regression to bacterial and fungal taxa associated with ragweed using different cutoff loadings for partial least square components. Upper and lower x-axes indicate the cutoff loading score for three components, number indicates the absolute loading score, Q5% indicates

In document Feedback by the soil microbial community on the distribution of two weeds (Page 55-90)